The MicroArray Explorer or MAExplorer is an open source Java-based data mining tool for exploring microarray DNA expression data across multiple hybridized microarrays. Arrays are used to monitor expression profiles under various physiological conditions. MAExplorer is available as a stand-alone application on a user's computer.( Lemkin PF, etal. (2000) Nucleic Acids Res. 28(22): 4452-4459. It reads data on the user's disk for creating custom data mining sessions which may be checkpointed for later use. It can handle data derived from a variety of microarrays, with 33P, Cy3/Cy5, clone and oligo arrays, and other labeling systems. A data conversion wizard program Cvt2Mae converts tab-delimited array data for use by MAExplorer. The NCI/CIT mAdb array depository system is able to generate exporteddata in MAExplorer format - ready to analyze. Data may be viewed and directly manipulated in images, scatter plots, histograms, expression profile plots, cluster analysis, etc. Interesting sets of clones may be discovered using a "data filter" that finds a set of clones passing a variety of user-specified tests. Users may generate filtered clone reports which may directly access UniGene, GeneBank, NCI/CIT mAdb and other Internet databases. Report data may be exported to Excel. MAExplorer helps: 1) analyze the expression of individual genes; 2) analyze the expression of gene families and clusters; 3) compare expression patterns for multiple arrays. MAExplorer has been made open source and is freelyavailable on http://maexplorer.sourceforge.net/. It may be downloaded the stand-alone version of MAExplorer for running on Windows, Macintosh or Unix systems (Sun Solaris, Linux, etc). The web site also contains documentation and tutorials. The source code is available for review or modification on the Web site. A Java plugin facility, MAEPlugins, is available to allow investigators to add their own analysis methods. It alsoallows plugins to be written in the R language so that users can write "R plugins" (we call these RLOs) to extend the analysis of MAExplorer data using many of the sophisticated statistics, clustering and analysis methods available for the R language.